A split-and-merge Bayesian variable selection approach for ultrahigh dimensional regression
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Cited by:
- Guangbao Guo & Yue Sun & Xuejun Jiang, 2020. "A partitioned quasi-likelihood for distributed statistical inference," Computational Statistics, Springer, vol. 35(4), pages 1577-1596, December.
- Hubin, Aliaksandr & Storvik, Geir, 2018. "Mode jumping MCMC for Bayesian variable selection in GLMM," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 281-297.
- Guangbao Guo & Guoqi Qian & Lu Lin & Wei Shao, 2021. "Parallel inference for big data with the group Bayesian method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 225-243, February.
- Lee, JooChul & Wang, HaiYing & Schifano, Elizabeth D., 2020. "Online updating method to correct for measurement error in big data streams," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
- Fang, Jianglin, 2023. "A split-and-conquer variable selection approach for high-dimensional general semiparametric models with massive data," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
- Jaeger, Adam & Lazar, Nicole A., 2020. "Split sample empirical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
- Runmin Shi & Faming Liang & Qifan Song & Ye Luo & Malay Ghosh, 2018. "A Blockwise Consistency Method for Parameter Estimation of Complex Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 179-223, December.
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